Discover how LinkedIn is operationalizing AI tools at scale to drive measurable productivity gains across its engineering organization.

This session explores how LinkedIn is building AI agents that collaborate with each other and humans to assist with core software development tasks such as writing code and refactoring, and how we leverage standardized developer environments to make agent execution safe, observable, and scalable across teams. Central to this effort is the use of the Model Context Protocol (MCP) to unify access across diverse models and tools, enabling consistent context injection, reliable agent behavior, and seamless interoperability across internal and external systems.

The talk also covers LinkedIn’s hybrid model strategy—balancing internally hosted, fine-tuned open source models for sensitive workloads with externally hosted commercial foundation models for broader reasoning tasks—alongside robust model governance and lifecycle management practices. 

The talk will discuss real-world challenges around context engineering techniques that structure enterprise data for meaningful AI reasoning, and strategies for addressing authentication and authorization when agents act on behalf of employees in secure environments. 

Gain insights into the architectural and infrastructural foundations that turn generative AI from promise to productivity.